Ben-Akiva, M

Kamargianni, M

Matyas, M

Pereira, F

Raman, S

Zhao, F

UCL Discovery

Big	  Data	  Collection	  using	  Smartphone	  Based	  Surveys	  and	  Open	  APIs:	  the	  Future	  Mobility	  Sensing	  for	  London	  	   Availability	  of	   rich	  and	  high	  quality	  data	   is	   a	  key	  driver	   in	  every	   sector.	  Transportation	   is	   no	   exception,	   where	   the	   way	   data	   is	   created,	   collected,	  analysed	  and	  shared	  is	  immensely	  evolving.	  Technological	  progress	  has	  allowed	  us	   to	   shift	   away	   from	  historically	  manual	   and	   spatially	   fixed	   data	   collection	   to	  dynamic	  methods.	  In	  the	  field	  of	  travel	  behaviour,	  traditional	  face-­‐to-­‐face	  paper	  based	  surveys	  have	  developed	   to	  exploit	   the	  advancements	   in	   information	  and	  communication	   technologies	   (ICT).	   The	   rapid	   penetration	   of	   location-­‐aware	  smartphones	   has	   made	   researchers	   realise	   the	   potential	   of	   these	   devices	   to	  collect	   individual	   level	  travel	  data.	  As	  such,	  numerous	  smartphone	  based	  travel	  survey	  tools	  have	  emerged	  in	  recent	  years	  that	  track	  the	  trips	  of	  individuals	  and	  offer	   precise	   spatial	   and	   temporal	   information.	   These	   new	   collection	  methods	  also	   have	   significant	   benefits	   regarding	   the	   survey	   experience	   of	   the	   user.	  Respondent	   burden	   is	   decreased	   by	   the	   tools	   automatically	   recording	   exact	  times	   and	   locations	   without	   user	   input,	   and	   by	  machine	   learning	   aiding	   long-­‐term	   data	   collection.	   Further,	   these	   computerised	   systems	   also	   have	   huge	  untapped	  potential,	  as	  they	  can	  be	  used	  to	  harness	  information	  from	  any	  source.	  	  One	   of	   these	   sources	   is	   the	   vast	   amount	   of	   open	   data	   and	   APIs	  (Application	  Programming	  Interfaces)	  available	   for	  developers.	  The	  quality	  and	  quantity	   of	   such	   open	   datasets	   are	   constantly	   improving,	   expanding	   and	  becoming	  more	  dynamic	  through	  live	  feeds	  Given	  the	  wealth	  of	  open	  transport	  APIs,	   the	   first	  aim	  of	   this	  study	   is	   to	  investigate	  and	  propose	  a	  methodology	  to	   link	  open	  APIs	   to	  smartphone	  based	  travel	  survey	  tools	  to	  improve	  the	  quantity	  and	  the	  quality	  of	  the	  collected	  data	  and	   to	   further	   reduce	   response	   burden.	   By	   connecting	   the	   diary	   with	   openly	  available	   transport	   operators’	   APIs,	   rich	   data	   can	   be	   harnessed	   on	   top	   of	   the	  locational	   data,	   such	   as	   travel	   costs,	   network	   conditions,	   availability	   and	  characteristics	  of	  alternative	  transport	  modes.	  Greater	  London	  is	  taken	  as	  a	  case	  study	  where	   the	   Future	  Mobility	   Sensing	   tool	   is	   connected	  with	   Transport	   for	  London’s	   (TfL)	   open	   data	   and	   APIs	   as	  well	   as	   Google	   open	   APIs.	   The	   complex	  way	  public	   transport	   fares	  are	  calculated	  makes	   it	  very	  difficult	   to	  get	  accurate	  cost	   estimates	   from	   respondents.	   However,	   for	   any	   modelling	   exercise	   travel	  costs	   are	   an	   essential	   variable.	   Against	   this	   background,	   the	   focal	   point	   of	   this	  research	  is	  to	  connect	  the	  public	  transport	  operator’s	  (TfL’s)	  cost	  API’s	  to	  create	  instantaneous,	   automatic	   and	   accurate	   travel	   cost	   values	   for	   public	   transport	  trips	   taking	   into	   account	   factors	   such	   as	   zoning,	   transfers	   and	   peak/off-­‐peak	  pricing.	  Besides	   decreasing	   respondent	   burden,	   survey	   experience	   can	   also	   be	  enhanced	  by	  making	   the	  questionnaires	  more	   interactive.	  Providing	  users	  with	  in-­‐survey	  feedback	  can	  increase	  the	  respondents’	  willingness	  to	  participate	  (2).	  	  These	   methods	   can	   increase	   response	   and	   completion	   rates,	   as	   it	   gives	  respondents	  motivation	  and	  a	  reward.	  Thus,	  the	  second	  aim	  of	  this	  research	  is	  to	  utilize	  the	  above-­‐mentioned	  travel	  costs,	  as	  well	  as	  other	  information	  collected,	  and	  describe	  an	  approach	  for	  presenting	  respondents	  with	  summary	  records	  of	  their	  travel.	  	  

Big Data Collection using Smartphone Based Surveys and Open APIs: the Future Mobility Sensing for London

http://discovery.ucl.ac.uk/10037894/1/ESTMOBS16_Big%20Data%20Collection_abstract.pdf

Big Data Collection using Smartphone Based Surveys and Open APIs: the Future Mobility Sensing for London

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